INTERNATIONAL CONFERENCE ON HARMONISATION OF TECHNICALREQUIREMENTS FOR REGISTRATION OF PHARMACEUTICALS FOR HUMAN
USE

ICH HARMONISED TRIPARTITE GUIDELINEEVALUATION FOR STABILITY DATA
Q1E
Current Step 4 version
dated 6 February 2003

This Guideline has been developed by the appropriate ICH Expert Working Groupand has been subject to consultation by the regulatory parties, in accordance with the
ICH Process. At Step 4 of the Process the final draft is recommended for adoption to
the regulatory bodies of the European Union, Japan and USA.

Q1E
Document History
First
Codification
Q1E

History

DateApproval by the Steering Committee under Step 2 andrelease for public consultation.

New
Codification
November
2005

6 February
2002

Q1E

6 February
2003

Q1E

Current Step 4 version
Q1E

Approval by the Steering Committee under Step 4 andrecommendation for adoption to the three ICH regulatory
bodies.

ii

EVALUATION FOR STABILITY DATAICH Harmonised Tripartite Guideline
Having reached Step 4 of the ICH Process at the ICH Steering Committee meetingon 6 February 2003, this guideline is recommended for
adoption to the three regulatory parties to ICH

TABLE OF CONTENTS
1.

INTRODUCTION.................................................................................................... 1
1.1

Objectives of the Guideline ........................................................................................1

1.2

Background.................................................................................................................1

1.3

Scope of the Guideline................................................................................................1

2.

3.

GUIDELINES .......................................................................................................... 1
2.1

General Principles......................................................................................................1

2.2

Data presentation ......................................................................................................3

2.3

Extrapolation..............................................................................................................3

2.4

Data Evaluation for Retest Period or Shelf Life Estimation for Drug
Substances or Products Intended for Room Temperature Storage .........................3

2.4.1

No significant change at accelerated condition ........................................................3

2.4.2

Significant change at accelerated condition .............................................................5

2.5

Data Evaluation for Retest Period or Shelf Life Estimation for Drug
Substances or Products Intended for Storage Below Room Temperature ..............5

2.5.1

Drug substances or products intended for storage in a refrigerator .......................5

2.5.2

Drug substances or products intended for storage in a freezer ...............................6

2.5.3

Drug substances or products intended for storage below -20°C ..............................7

2.6

General Statistical Approaches.................................................................................7
APPENDICES ......................................................................................................... 8

Appendix A:
Decision Tree for Data Evaluation for Retest Period or Shelf Life
Estimation for Drug Substances or Products (excluding Frozen Products) .........................8
Appendix B: Examples of Statistical Approaches to Stability Data Analysis .....................8i

EVALUATION OF STABILITY DATA1.

INTRODUCTION

1.1
Objectives of the Guideline
This guideline is intended to provide recommendations on how to use stability data
generated in accordance with the principles detailed in the ICH guideline “Q1A(R)Stability Testing of New Drug Substances and Products” (hereafter referred to as theparent guideline) to propose a retest period or shelf life in a registration application.
This guideline describes when and how extrapolation can be considered when
proposing a retest period for a drug substance or a shelf life for a drug product that
extends beyond the period covered by “available data from the stability study under
the long-term storage condition” (hereafter referred to as long-term data).
1.2
BackgroundThe guidance on the evaluation and statistical analysis of stability data provided in
the parent guideline is brief in nature and limited in scope. The parent guideline
states that regression analysis is an appropriate approach to analyzing quantitative
stability data for retest period or shelf life estimation and recommends that astatistical test for batch poolability be performed using a level of significance of 0.25.
However, the parent guideline includes few details and does not cover situations
where multiple factors are involved in a full- or reduced-design study.
This guideline is an expansion of the guidance presented in the Evaluation sections of
the parent guideline.
1.3
Scope of the Guideline
This guideline addresses the evaluation of stability data that should be submitted inregistration applications for new molecular entities and associated drug products. The
guideline provides recommendations on establishing retest periods and shelf lives for
drug substances and drug products intended for storage at or below “roomtemperature”*. It covers stability studies using single- or multi-factor designs and fullor reduced designs.
*Note: The term “room temperature” refers to the general customary environmentand should not be inferred to be the storage statement for labeling.
ICH Q6A and Q6B should be consulted for recommendations on the setting andjustification of acceptance criteria, and ICH Q1D should be referenced forrecommendations on the use of full- versus reduced-design studies.
2.

GUIDELINES

2.1
General Principles
The design and execution of formal stability studies should follow the principles
outlined in the parent guideline. The purpose of a stability study is to establish, basedon testing a minimum of three batches of the drug substance or product, a retest
period or shelf life and label storage instructions applicable to all future batches
manufactured and packaged under similar circumstances. The degree of variability of
individual batches affects the confidence that a future production batch will remain
within acceptance criteria throughout its retest period or shelf life.

1Evaluation of Stability Data

Although normal manufacturing and analytical variations are to be expected, it isimportant that the drug product be formulated with the intent to provide 100 percentof the labeled amount of the drug substance at the time of batch release. If the assay
values of the batches used to support the registration application are higher than 100
percent of label claim at the time of batch release, after taking into accountmanufacturing and analytical variations, the shelf life proposed in the application canbe overestimated. On the other hand, if the assay value of a batch is lower than 100
percent of label claim at the time of batch release, it might fall below the lower
acceptance criterion before the end of the proposed shelf life.
A systematic approach should be adopted in the presentation and evaluation of thestability information. The stability information should include, as appropriate, resultsfrom the physical, chemical, biological, and microbiological tests, including thoserelated to particular attributes of the dosage form (for example, dissolution rate forsolid oral dosage forms). The adequacy of the mass balance should be assessed.
Factors that can cause an apparent lack of mass balance should be considered,including, for example, the mechanisms of degradation and the stability-indicating
capability and inherent variability of the analytical procedures.
The basic concepts of stability data evaluation are the same for single- versus multifactor studies and for full- versus reduced-design studies. Data from formal stability
studies and, as appropriate, supporting data should be evaluated to determine thecritical quality attributes likely to influence the quality and performance of the drug
substance or product. Each attribute should be assessed separately, and an overall
assessment should be made of the findings for the purpose of proposing a retest period
or shelf life. The retest period or shelf life proposed should not exceed that predicted
for any single attribute.
The decision tree in Appendix A outlines a stepwise approach to stability data
evaluation and when and how much extrapolation can be considered for a proposed
retest period or shelf life. Appendix B provides (1) information on how to analyze longterm data for appropriate quantitative test attributes from a study with a multifactor, full or reduced design, (2) information on how to use regression analysis forretest period or shelf life estimation, and (3) examples of statistical procedures todetermine poolability of data from different batches or other factors. Additional
guidance can be found in the references listed; however, the examples and references
do not cover all applicable statistical approaches.
In general, certain quantitative chemical attributes (e.g., assay, degradation products,
preservative content) for a drug substance or product can be assumed to follow zeroorder kinetics during long-term storage1. Data for these attributes are therefore
amenable to the type of statistical analysis described in Appendix B, including linear
regression and poolability testing. Although the kinetics of other quantitative
attributes (e.g., pH, dissolution) is generally not known, the same statistical analysis
can be applied, if appropriate. Qualitative attributes and microbiological attributes
are not amenable to this kind of statistical analysis.
The recommendations on statistical approaches in this guideline are not intended to
imply that use of statistical evaluation is preferred when it can be justified to beunnecessary. However, statistical analysis can be useful in supporting theextrapolation of retest periods or shelf lives in certain situations and can be called forto verify the proposed retest periods or shelf lives in other cases.

2

Evaluation of Stability Data

2.2
Data presentation
Data for all attributes should be presented in an appropriate format (e.g., tabular,
graphical, narrative) and an evaluation of such data should be included in theapplication. The values of quantitative attributes at all time points should be reported
as measured (e.g., assay as percent of label claim). If a statistical analysis isperformed, the procedure used and the assumptions underlying the model should bestated and justified. A tabulated summary of the outcome of statistical analysis
and/or graphical presentation of the long-term data should be included.
2.3
Extrapolation
Extrapolation is the practice of using a known data set to infer information about
future data. Extrapolation to extend the retest period or shelf life beyond the period
covered by long-term data can be proposed in the application, particularly if nosignificant change is observed at the accelerated condition. Whether extrapolation ofstability data is appropriate depends on the extent of knowledge about the change
pattern, the goodness of fit of any mathematical model, and the existence of relevant
supporting data. Any extrapolation should be performed such that the extended retest
period or shelf life will be valid for a future batch released with test results close to
the release acceptance criteria.
An extrapolation of stability data assumes that the same change pattern will continue
to apply beyond the period covered by long-term data. The correctness of the assumed
change pattern is critical when extrapolation is considered. When estimating a
regression line or curve to fit the long-term data, the data themselves provide a checkon the correctness of the assumed change pattern, and statistical methods can beapplied to test the goodness of fit of the data to the assumed line or curve. No suchinternal check is possible beyond the period covered by long-term data. Thus, a retest
period or shelf life granted on the basis of extrapolation should always be verified byadditional long-term stability data as soon as these data become available. Careshould be taken to include in the protocol for commitment batches a time point that
corresponds to the end of the extrapolated retest period or shelf life.
2.4

Data Evaluation for Retest Period or Shelf Life Estimation for Drug
Substances or Products Intended for Room Temperature Storage
A systematic evaluation of the data from formal stability studies should be performed
as illustrated in this section. Stability data for each attribute should be assessedsequentially. For drug substances or products intended for storage at room
temperature, the assessment should begin with any significant change at theaccelerated condition and, if appropriate, at the intermediate condition, and progress
through the trends and variability of the long-term data. The circumstances aredelineated under which extrapolation of retest period or shelf life beyond the period
covered by long-term data can be appropriate. A decision tree is provided in Appendix
A as an aid.
2.4.1 No significant change at accelerated condition
Where no significant change occurs at the accelerated condition, the retest period or
shelf life would depend on the nature of the long-term and accelerated data.

3

Evaluation of Stability Data

2.4.1.1 Long-term and accelerated data showing little or no change over timeand little or no variability
Where the long-term data and accelerated data for an attribute show little or nochange over time and little or no variability, it might be apparent that the drug
substance or product will remain well within the acceptance criteria for that attributeduring the proposed retest period or shelf life. In these circumstances, a statisticalanalysis is normally considered unnecessary but justification for the omission should
be provided. Justification can include a discussion of the change pattern or lack ofchange, relevance of the accelerated data, mass balance, and/or other supporting data
as described in the parent guideline. Extrapolation of the retest period or shelf life
beyond the period covered by long-term data can be proposed. The proposed retest
period or shelf life can be up to twice, but should not be more than 12 months beyond,the period covered by long-term data.
2.4.1.2 Long-term or accelerated data showing change over time and/or
variability
If the long-term or accelerated data for an attribute show change over time and/or
variability within a factor or among factors, statistical analysis of the long-term data
can be useful in establishing a retest period or shelf life. Where there are differencesin stability observed among batches or among other factors (e.g., strength, container
size and/or fill) or factor combinations (e.g., strength-by-container size and/or fill) that
preclude the combining of data, the proposed retest period or shelf life should notexceed the shortest period supported by any batch, other factor, or factor combination.
Alternatively, where the differences are readily attributed to a particular factor (e.g.,strength), different shelf lives can be assigned to different levels within the factor(e.g., different strengths). A discussion should be provided to address the cause for thedifferences and the overall significance of such differences on the product.
Extrapolation beyond the period covered by long-term data can be proposed; however,
the extent of extrapolation would depend on whether long-term data for the attributeare amenable to statistical analysis.
•

Data not amenable to statistical analysis

Where long-term data are not amenable to statistical analysis, but relevant
supporting data are provided, the proposed retest period or shelf life can be up to oneand-a-half times, but should not be more than 6 months beyond, the period covered bylong-term data. Relevant supporting data include satisfactory long-term data fromdevelopment batches that are (1) made with a closely related formulation to, (2)manufactured on a smaller scale than, or (3) packaged in a container closure system
similar to, that of the primary stability batches.
•

Data amenable to statistical analysis

If long-term data are amenable to statistical analysis but no analysis is performed,
the extent of extrapolation should be the same as when data are not amenable tostatistical analysis. However, if a statistical analysis is performed, it can beappropriate to propose a retest period or shelf life of up to twice, but not more than 12
months beyond, the period covered by long-term data, when the proposal is backed by
the result of the analysis and relevant supporting data.

4

Evaluation of Stability Data

2.4.2 Significant change at accelerated condition
Where significant change* occurs at the accelerated condition, the retest period or
shelf life would depend on the outcome of stability testing at the intermediate
condition, as well as at the long-term condition.
*Note: The following physical changes can be expected to occur at the accelerated
condition and would not be considered significant change that calls for intermediate
testing if there is no other significant change:•

softening of a suppository that is designed to melt at 37ºC, if the melting point isclearly demonstrated,•

failure to meet acceptance criteria for dissolution for 12 units of a gelatin capsuleor gel-coated tablet if the failure can be unequivocally attributed to cross-linking.

However, if phase separation of a semi-solid dosage form occurs at the accelerated
condition, testing at the intermediate condition should be performed. Potential
interaction effects should also be considered in establishing that there is no othersignificant change.
2.4.2.1 No significant change at intermediate condition
If there is no significant change at the intermediate condition, extrapolation beyond
the period covered by long-term data can be proposed; however, the extent of
extrapolation would depend on whether long-term data for the attribute are amenable
to statistical analysis.
•

Data not amenable to statistical analysis

When the long-term data for an attribute are not amenable to statistical analysis, theproposed retest period or shelf life can be up to 3 months beyond the period covered bylong-term data, if backed by relevant supporting data.
•

Data amenable to statistical analysis

When the long-term data for an attribute are amenable to statistical analysis but noanalysis is performed, the extent of extrapolation should be the same as when data
are not amenable to statistical analysis. However, if a statistical analysis isperformed, the proposed retest period or shelf life can be up to one-and-half times, butshould not be more than 6 months beyond, the period covered by long-term data,when backed by statistical analysis and relevant supporting data.
2.4.2.2 Significant change at intermediate condition
Where significant change occurs at the intermediate condition, the proposed retest
period or shelf life should not exceed the period covered by long-term data. In
addition, a retest period or shelf life shorter than the period covered by long-term data
could be called for.
2.5

Data Evaluation for Retest Period or Shelf Life Estimation for Drug
Substances or Products Intended for Storage Below Room
Temperature

2.5.1 Drug substances or products intended for storage in a refrigerator
Data from drug substances or products intended to be stored in a refrigerator should
be assessed according to the same principles as described in Section 2.4 for drug

5

Evaluation of Stability Data

substances or products intended for room temperature storage, except where
explicitly noted in the section below. The decision tree in Appendix A can be used as
an aid.
2.5.1.1 No significant change at accelerated condition
Where no significant change occurs at the accelerated condition, extrapolation ofretest period or shelf life beyond the period covered by long-term data can be proposed
based on the principles outlined in Section 2.4.1, except that the extent of
extrapolation should be more limited.
If the long-term and accelerated data show little change over time and little
variability, the proposed retest period or shelf life can be up to one-and-a-half times,
but should not be more than 6 months beyond, the period covered by long-term datanormally without the support of statistical analysis.
Where the long-term or accelerated data show change over time and/or variability, theproposed retest period or shelf life can be up to 3 months beyond the period covered bylong-term data if (1) the long-term data are amenable to statistical analysis but astatistical analysis is not performed, or (2) the long-term data are not amenable tostatistical analysis but relevant supporting data are provided.
Where the long-term or accelerated data show change over time and/or variability, theproposed retest period or shelf life can be up to one-and-a-half times, but should not
be more than 6 months beyond, the period covered by long-term data if (1) the longterm data are amenable to statistical analysis and a statistical analysis is performed,and (2) the proposal is backed by the result of the analysis and relevant supportingdata.
2.5.1.2 Significant change at accelerated condition
If significant change occurs between 3 and 6 months’ testing at the accelerated
storage condition, the proposed retest period or shelf life should be based on the longterm data. Extrapolation is not considered appropriate. In addition, a retest period or
shelf life shorter than the period covered by long-term data could be called for. If thelong-term data show variability, verification of the proposed retest period or shelf life
by statistical analysis can be appropriate.
If significant change occurs within the first 3 months’ testing at the accelerated
storage condition, the proposed retest period or shelf life should be based on long-term
data. Extrapolation is not considered appropriate. A retest period or shelf life shorter
than the period covered by long-term data could be called for. If the long-term data
show variability, verification of the proposed retest period or shelf life by statisticalanalysis can be appropriate. In addition, a discussion should be provided to address
the effect of short-term excursions outside the label storage condition (e.g., during
shipping or handling). This discussion can be supported, if appropriate, by further
testing on a single batch of the drug substance or product at the accelerated condition
for a period shorter than 3 months.
2.5.2 Drug substances or products intended for storage in a freezer
For drug substances or products intended for storage in a freezer, the retest period or
shelf life should be based on long-term data. In the absence of an accelerated storage
condition for drug substances or products intended to be stored in a freezer, testing ona single batch at an elevated temperature (e.g., 5°C ± 3°C or 25°C ± 2°C) for an

6

Evaluation of Stability Data

appropriate time period should be conducted to address the effect of short-term
excursions outside the proposed label storage condition (e.g., during shipping or
handling).
2.5.3 Drug substances or products intended for storage below -20°C
For drug substances or products intended for storage below -20°C, the retest period or
shelf life should be based on long-term data and should be assessed on a case-by-casebasis.
2.6
General Statistical Approaches
Where applicable, an appropriate statistical method should be employed to analyze
the long-term primary stability data in an original application. The purpose of thisanalysis is to establish, with a high degree of confidence, a retest period or shelf life
during which a quantitative attribute will remain within acceptance criteria for all
future batches manufactured, packaged, and stored under similar circumstances.
In cases where a statistical analysis was employed to evaluate long-term data due to achange over time and/or variability, the same statistical method should also be used
to analyse data from commitment batches to verify or extend the originally approved
retest period or shelf life.
Regression analysis is considered an appropriate approach to evaluating the stability
data for a quantitative attribute and establishing a retest period or shelf life. The
nature of the relationship between an attribute and time will determine whether data
should be transformed for linear regression analysis. The relationship can be
represented by a linear or non-linear function on an arithmetic or logarithmic scale.
In some cases, a non-linear regression can better reflect the true relationship.
An appropriate approach to retest period or shelf life estimation is to analyze aquantitative attribute (e.g., assay, degradation products) by determining the earliest
time at which the 95 percent confidence limit for the mean intersects the proposed
acceptance criterion.
For an attribute known to decrease with time, the lower one-sided 95 percent
confidence limit should be compared to the acceptance criterion. For an attributeknown to increase with time, the upper one-sided 95 percent confidence limit should
be compared to the acceptance criterion. For an attribute that can either increase or
decrease, or whose direction of change is not known, two-sided 95 percent confidence
limits should be calculated and compared to the upper and lower acceptance criteria.
The statistical method used for data analysis should take into account the stability
study design to provide a valid statistical inference for the estimated retest period or
shelf life. The approach described above can be used to estimate the retest period or
shelf life for a single batch or for multiple batches when the data are combined after
an appropriate statistical test. Examples of statistical approaches to the analysis ofstability data from single or multi-factor, full- or reduced-design studies are includedin Appendix B. References to current literature sources can be found in Appendix B.6.

7

Evaluation of Stability Data

3.

APPENDICES

Appendix A: Decision Tree for Data Evaluation for Retest Period or Shelf
Life Estimation for Drug Substances or Products (excluding Frozen
Products)
Tabulate and/or plot
stability data on all
attributes at all storage
conditions and evaluateeach attribute separately

No extrapolation; shorter
retest period or shelf life anddata covering excursions
can be called for; statisticalanalysis if long-term data
show variability

Yes

Significant
change at accelerated
condition within
3 months?

No

Yes

Significant
change at accelerated
condition within
6 months?

Yes

Significant
changeat intermediate
condition?

No

Intended
to be stored in arefrigerator?

Yes

No extrapolation; shorter
retest period or shelf life
can be called for; statisticalanalysis if long-term data
show variability

No
No

Long- term
data show: (1) little orno change over timeand (2) little or no
variability?

(1)Long- term
data amenable tostatistical analysis and(2) statistical analysis
performed?

No to (1) or(2) or both

Yes to both

Accelerated
data show: (1) little orno change over timeand (2) little or no
variability?

Yes to bothor (2)

If backed by relevant
supporting data:
Y = up to X + 3 months

Yes to both

No to (1) or(2) or both(1)Long- term
data amenable tostatistical analysis and(2) statistical analysis
performed?

No to (1)or (2)

If backed by statisticalanalysis and relevant
supporting data: Y = up
to 1.5X, but notexceeding X + 6 months

Yes to both
Y = Proposed retest period or shelf life
X = Period covered by long-term data

Statistical analysis
is normallyunnecessary

Y= up to 2X, but notexceeding X + 12 months;
or if refrigerated,
Y = up to 1.5X, but notexceeding X + 6 months

No to (1)

If backed by statisticalanalysis and relevant
supporting data: Y = up to
2X, but not exceeding X +12 months; or if refrigerated,
Y = up to 1.5X, but notexceeding X + 6 months

8

If backed by relevant
supporting data: Y = up
to 1.5X, but notexceeding X + 6 months;
or if refrigerated, Y = up
to X + 3 months

Evaluation of Stability Data

Appendix B: Examples of Statistical Approaches to Stability Data Analysis
Linear regression, poolability tests, and statistical modeling, described below, areexamples of statistical methods and procedures that can be used in the analysis ofstability data that are amenable to statistical analysis for a quantitative attribute forwhich there is a proposed acceptance criterion.
B.1
Data Analysis for a Single Batch
In general, the relationship between certain quantitative attributes and time isassumed to be linear1. Figure 1 shows the regression line for assay of a drug productwith upper and lower acceptance criteria of 105 percent and 95 percent of label claim,
respectively, with 12 months of long-term data and a proposed shelf life of 24 months.
In this example, two-sided 95 percent confidence limits for the mean are applied
because it is not known ahead of time whether the assay would increase or decrease
with time (e.g., in the case of an aqueous-based product packaged in a semi-permeablecontainer). The lower confidence limit intersects the lower acceptance criterion at 30
months, while the upper confidence limit does not intersect with the upper acceptancecriterion until later. Therefore, the proposed shelf life of 24 months can be supportedby the statistical analysis of the assay, provided the recommendations in Sections 2.4
and 2.5 are followed.
When data for an attribute with only an upper or a lower acceptance criterion are
analyzed, the corresponding one-sided 95 percent confidence limit for the mean isrecommended. Figure 2 shows the regression line for a degradation product in a drug
product with 12 months of long-term data and a proposed shelf life of 24 months,
where the acceptance criterion is not more than 1.4 percent. The upper one-sided 95
percent confidence limit for the mean intersects the acceptance criterion at 31
months. Therefore, the proposed shelf life of 24 months can be supported bystatistical analysis of the degradation product data, provided the recommendations in
Sections 2.4 and 2.5 are followed.
If the above approach is used, the mean value of the quantitative attribute (e.g.,assay, degradation products) can be expected to remain within the acceptance criteria
through the end of the retest period or shelf life at a confidence level of 95 percent.
The approach described above can be used to estimate the retest period or shelf life fora single batch, individual batches, or multiple batches when combined after
appropriate statistical tests described in Sections B.2 through B.5.
B.2
Data Analysis for One-Factor, Full-Design Studies
For a drug substance or for a drug product available in a single strength and a single
container size and/or fill, the retest period or shelf life is generally estimated based on
the stability data from a minimum of three batches. When analyzing data from such
one-factor, batch-only, full-design studies, two statistical approaches can beconsidered.
•

The objective of the first approach is to determine whether the data from allbatches support the proposed retest period or shelf life.

•

The objective of the second approach, testing for poolability, is to determine
whether the data from different batches can be combined for an overall estimate ofa single retest period or shelf life.

9Evaluation of Stability Data

B.2.1 Evaluating whether all batches support the proposed retest period or
shelf life
The objective of this approach is to evaluate whether the estimated retest periods or
shelf lives from all batches are longer than the one proposed. Retest periods or shelf
lives for individual batches should first be estimated using the procedure described in
Section B.1 with individual intercepts, individual slopes, and the pooled mean squareerror calculated from all batches. If each batch has an estimated retest period or shelf
life longer than that proposed, the proposed retest period or shelf life will generally beconsidered appropriate, as long as the guidance for extrapolation in Sections 2.4 and
2.5 is followed. There is generally no need to perform poolability tests or identify themost reduced model. If, however, one or more of the estimated retest periods or shelf
lives are shorter than that proposed, poolability tests can be performed to determine
whether the batches can be combined to estimate a longer retest period or shelf life.
Alternatively, the above approach can be taken during the pooling process describedin Section B.2.2. If the regression lines for the batches are found to have a common
slope and the estimated retest periods or shelf lives based on the common slope andindividual intercepts are all longer than the proposed retest period or shelf life, there
is generally no need to continue to test the intercepts for poolability.
B.2.2 Testing for poolability of batches
B.2.2.1 Analysis of covariance
Before pooling the data from several batches to estimate a retest period or shelf life, apreliminary statistical test should be performed to determine whether the regression
lines from different batches have a common slope and a common time-zero intercept.
Analysis of covariance (ANCOVA) can be employed, where time is considered thecovariate, to test the differences in slopes and intercepts of the regression lines amongbatches. Each of these tests should be conducted using a significance level of 0.25 tocompensate for the expected low power of the design due to the relatively limited
sample size in a typical formal stability study.
If the test rejects the hypothesis of equality of slopes (i.e., if there is a significant
difference in slopes among batches), it is not considered appropriate to combine thedata from all batches. The retest periods or shelf lives for individual batches in thestability study can be estimated by applying the approach described in Section B.1
using individual intercepts and individual slopes and the pooled mean square error
calculated from all batches. The shortest estimate among the batches should bechosen as the retest period or shelf life for all batches.
If the test rejects the hypothesis of equality of intercepts but fails to reject that theslopes are equal (i.e., if there is a significant difference in intercepts but no significant
difference in slopes among the batches), the data can be combined for the purpose of
estimating the common slope. The retest periods or shelf lives for individual batches
in the stability study should be estimated by applying the approach described in
Section B.1, using the common slope and individual intercepts. The shortest estimate
among the batches should be chosen as the retest period or shelf life for all batches.
If the tests for equality of slopes and equality of intercepts do not result in rejection at
a level of significance of 0.25 (i.e., if there is no significant difference in slope andintercepts among the batches), the data from all batches can be combined. A single
retest period or shelf life can be estimated from the combined data by using the10

Evaluation of Stability Data

approach described in Section B.1 and applied to all batches. The estimated retest
period or shelf life from the combined data is usually longer than that from individual
batches because the width of the confidence limit(s) for the mean will become
narrower as the amount of data increases when batches are combined.
The pooling tests described above should be performed in a proper order such that theslope terms are tested before the intercept terms. The most reduced model (i.e.,
individual slopes, common slope with individual intercepts, or common slope with
common intercept, as appropriate) can be selected for retest period or shelf life
estimation.
B.2.2.2 Other methods
Statistical procedures2-6 other than those described above can be used in retest period
or shelf life estimation. For example, if it is possible to decide in advance theacceptable difference in slope or in mean retest period or shelf life among batches, anappropriate procedure for assessing the equivalence in slope or in mean retest period
or shelf life can be used to determine the data poolability. However, such a procedure
should be prospectively defined, evaluated, and justified and, where appropriate,
discussed with the regulatory authority. A simulation study can be useful, if
applicable, to demonstrate that the statistical properties of the alternative procedure
selected are appropriate7.
B.3
Data Analysis for Multi-Factor, Full-Design Studies
The stability of the drug product could differ to a certain degree among different
factor combinations in a multi-factor, full-design study. Two approaches can beconsidered when analyzing such data.
•

The objective of the first approach is to determine whether the data from all factor
combinations support the proposed shelf life.

•

The objective of the second approach, testing for poolability, is to determine
whether the data from different factor combinations can be combined for an
overall estimate of a single shelf life.

B.3.1 Evaluating whether all factor combinations support the proposed
shelf life
The objective of this approach is to evaluate whether the estimated shelf lives from all
factor combinations are longer than the one proposed. A statistical model that
includes all appropriate factors and factor combinations should be constructed as
described in Section B.3.2.2.1, and the shelf life should be estimated for each level ofeach factor and factor combination.
If all shelf lives estimated by the original model are longer than the proposed shelflife, further model building is considered unnecessary and the proposed shelf life will
generally be appropriate as long as the guidance in Sections 2.4 and 2.5 is followed. If
one or more of the estimated shelf lives fall short of the proposed shelf life, modelbuilding as described in Section B.3.2.2.1 can be employed. However, it is considered
unnecessary to identify the final model before evaluating whether the data support
the proposed shelf life. Shelf lives can be estimated at each stage of the modelbuilding process, and if all shelf lives at any stage are longer than the one proposed,
further attempts to reduce the model are considered unnecessary.

11Evaluation of Stability Data

This approach can simplify the data analysis of a complicated multi-factor stability
study compared to the data analysis described in Section B.3.2.2.1.
B.3.2 Testing for poolability
The stability data from different combinations of factors should not be combined
unless supported by statistical tests for poolability.
B.3.2.1 Testing for poolability of batch factor only
If each factor combination is considered separately, the stability data can be tested forpoolability of batches only, and the shelf life for each non-batch factor combination
can be estimated separately by applying the procedure described in Section B.2. For
example, for a drug product available in two strengths and four container sizes, eightsets of data from the 2x4 strength-size combinations can be analyzed and eight
separate shelf lives should be estimated accordingly. If a single shelf life is desired,
the shortest estimated shelf life among all factor combinations should become theshelf life for the product. However, this approach does not take advantage of theavailable data from all factor combinations, thus generally resulting in shorter shelf
lives than does the approach in Section B.3.2.2.
B.3.2.2 Testing for poolability of all factors and factor combinations
If the stability data are tested for poolability of all factors and factor combinations
and the results show that the data can be combined, a single shelf life longer thanthat estimated based on individual factor combinations is generally obtainable. The
shelf life is longer because the width of the confidence limit(s) for the mean will
become narrower as the amount of data increases when batches, strengths, container
sizes and/or fills, etc. are combined.
B.3.2.2.1 Analysis of covariance
Analysis of covariance can be employed to test the difference in slopes and interceptsof the regression lines among factors and factor combinations7, 8. The purpose of theprocedure is to determine whether data from multiple factor combinations can be
combined for the estimation of a single shelf life.
The full statistical model should include the intercept and slope terms of all main
effects and interaction effects and a term reflecting the random error of measurement.
If it can be justified that the higher order interactions are very small, there is
generally no need to include these terms in the model. In cases where the analyticalresults at the initial time point are obtained from the finished dosage form prior to its
packaging, the container intercept term can be excluded from the full model because
the results are common among the different container sizes and/or fills.
The tests for poolability should be specified to determine whether there arestatistically significant differences among factors and factor combinations. Generally,
the pooling tests should be performed in a proper order such that the slope terms aretested before the intercept terms and the interaction effects are tested before the main
effects. For example, the tests can start with the slope and then the intercept terms of
the highest order interaction, and proceed to the slope and then the intercept terms of
the simple main effects. The most reduced model, obtained when all remaining terms
are found to be statistically significant, can be used to estimate the shelf lives.

12

Evaluation of Stability Data

All tests should be conducted using appropriate levels of significance. It is
recommended that a significance level of 0.25 be used for batch-related terms, and asignificance level of 0.05 be used for non-batch-related terms. If the tests forpoolability show that the data from different factor combinations can be combined, theshelf life can be estimated according to the procedure described in Section B.1 using
the combined data.
If the tests for poolability show that the data from certain factors or factor
combinations should not be combined, either of two alternatives can be applied: (1) aseparate shelf life can be estimated for each level of the factors and of the factor
combinations remaining in the model; or (2) a single shelf life can be estimated basedon the shortest estimated shelf life among all levels of factors and factor combinations
remaining in the model.
B.3.2.2.2 Other methods
Alternative statistical procedures2-6 to those described above can be applied. For
example, an appropriate procedure for assessing the equivalence in slope or in mean
shelf life can be used to determine the data poolability. However, such a procedure
should be prospectively defined, evaluated, properly justified, and, where appropriate,
discussed with the regulatory authority. A simulation study can be useful, if
applicable, to demonstrate that the statistical properties of the alternative procedure
selected are appropriate7.
B.4
Data Analysis For Bracketing Design Studies
The statistical procedures described in Section B.3 can be applied to the analysis ofstability data obtained from a bracketing design study. For example, for a drug
product available in three strengths (S1, S2, and S3) and three container sizes (P1,
P2, and P3) and studied according to a bracketing design where only the two extremesof the container sizes (P1 and P3) are tested, six sets of data from the 3x2 strengthsize combinations will be obtained. The data can be analyzed separately for each of
the six combinations for shelf life estimation according to Section B.3.2.1, or tested forpoolability prior to shelf life estimation according to Section B.3.2.2.
The bracketing design assumes that the stability of the intermediate strengths orsizes is represented by the stability at the extremes. If the statistical analysis
indicates that the stability of the extreme strengths or sizes is different, theintermediate strengths or sizes should be considered no more stable than the least
stable extreme. For example, if P1 from the above bracketing design is found to beless stable than P3, the shelf life for P2 should not exceed that for P1. Nointerpolation between P1 and P3 should be considered.
B.5
Data Analysis For Matrixing Design Studies
A matrixing design has only a fraction of the total number of samples tested at any
specified time point. Therefore, it is important to ascertain that all factors and factor
combinations that can have an impact on shelf life estimation have been
appropriately tested. For a meaningful interpretation of the study results and shelf
life estimation, certain assumptions should be made and justified. For instance, theassumption that the stability of the samples tested represents the stability of all
samples should be valid. In addition, if the design is not balanced, some factors orfactor interactions might not be estimable. Furthermore, for different levels of factor
combinations to be poolable, it might have to be assumed that the higher order factor

13Evaluation of Stability Datainteractions are negligible. Because it is usually impossible to statistically test theassumption that the higher order terms are negligible, a matrixing design should be
used only when it is reasonable to assume that these interactions are indeed verysmall, based on supporting data.
The statistical procedure described in Section B.3 can be applied to the analysis ofstability data obtained from a matrixing design study. The statistical analysis should
clearly identify the procedure and assumptions used. For instance, the assumptions
underlying the model in which interaction terms are negligible should be stated. If apreliminary test is performed for the purpose of eliminating factor interactions fromthe model, the procedure used should be provided and justified. The final model on
which the estimation of shelf life will be based should be stated. The estimation of
shelf life should be performed for each of the terms remaining in the model. The use ofa matrixing design can result in an estimated shelf life shorter than that resulting
from a full design.
Where bracketing and matrixing are combined in one design, the statistical proceduredescribed in Section B.3 can be applied.
B.6
1.

References
Carstensen, J.T., “Stability and Dating of Solid Dosage Forms”
Pharmaceutics of Solids and Solid Dosage Forms, Wiley-Interscience, 182-185,
1977

2.

Ruberg, S.J. and Stegeman, J.W., “Pooling Data for Stability Studies: Testing
the Equality of Batch Degradation Slopes”
Biometrics, 47:1059-1069, 1991

3.

Ruberg, S.J. and Hsu, J.C., “Multiple Comparison Procedures for Pooling
Batches in Stability Studies”
Technometrics, 34:465-472, 1992

4.

Shao, J. and Chow, S.C., “Statistical Inference in Stability Analysis”
Biometrics, 50:753-763, 1994

5.

Murphy, J.R. and Weisman, D., “Using Random Slopes for Estimating Shelflife”
Proceedings of American Statistical Association of the Biopharmaceutical
Section, 196-200, 1990

6.

Yoshioka, S., Aso, Y, and Kojima, S., “Assessment of Shelf-life Equivalence ofPharmaceutical Products”
Chem. Pharm. Bull., 45:1482-1484, 1997

7.

Chen, J.J., Ahn, H., and Tsong, Y., “Shelf-life Estimation for Multifactor
Stability Studies”
Drug Inf. Journal, 31:573-587, 19978.

Fairweather, W., Lin, T.D., and Kelly, R., “Regulatory, Design, and Analysis
Aspects of Complex Stability Studies”
J. Pharm. Sci., 84:1322-1326, 1995

14

Evaluation of Stability Data

B.7
FiguresFigure 1

Shelf life Estimation with Upper and Lower Acceptance Criteria Based on Assay at
25C/60%RH
120

Assay (% of Label Claim)

115
110
105
Raw Data
100

Upper confidence limit

95

Lower confidence limit
Regression line

90
85

Upper acceptancecriterion: 105

80

Lower acceptancecriterion: 95
0

3

6

9

12 15 18 21 24 27 30 33

36 39 42 45 48

Time Point (Months)

Figure 2
Shelf life Estimation with Upper Acceptance Criterion Based on a Degradation
Product at 25C/60%RH
3.0

Degradation Product (%)

2.5
2.0
1.5

Raw Data
Upper confidence limit

1.0

Regression line
Upper acceptancecriterion: 1.4

0.5
0.0
0

3

6

9

12

15

18

21

24

27

30

Time Point (Months)

15

33

36

39

42

45

48